Artificial pancreas (AP) systems have been shown to improve glycemic control throughout the day and night in adults, adolescents, and children. However, AP testing remains limited during intense and ...prolonged exercise in adolescents and children. We present the performance of the Tandem Control-IQ AP system in adolescents and children during a winter ski camp study, where high altitude, low temperature, prolonged intense activity, and stress challenged glycemic control.
In a randomized controlled trial, 24 adolescents (ages 13-18 years) and 24 school-aged children (6-12 years) with Type 1 diabetes (T1D) participated in a 48 hours ski camp (∼5 hours skiing/day) at three sites: Wintergreen, VA; Kirkwood, and Breckenridge, CO. Study participants were randomized 1:1 at each site. The control group used remote monitored sensor-augmented pump (RM-SAP), and the experimental group used the t: slim X2 with Control-IQ Technology AP system. All subjects were remotely monitored 24 hours per day by study staff.
The Control-IQ system improved percent time within range (70-180 mg/dL) over the entire camp duration: 66.4 ± 16.4 vs 53.9 ± 24.8%; P = .01 in both children and adolescents. The AP system was associated with a significantly lower average glucose based on continuous glucose monitor data: 161 ± 29.9 vs 176.8 ± 36.5 mg/dL; P = .023. There were no differences between groups for hypoglycemia exposure or carbohydrate interventions. There were no adverse events.
The use of the Control-IQ AP improved glycemic control and safely reduced exposure to hyperglycemia relative to RM-SAP in pediatric patients with T1D during prolonged intensive winter sport activities.
The primary objective of this trial was to evaluate the feasibility, safety, and efficacy of a predictive hyperglycemia and hypoglycemia minimization (PHHM) system vs predictive low glucose ...suspension (PLGS) alone in optimizing overnight glucose control in children 6 to 14 years old.
Twenty-eight participants 6 to 14 years old with T1D duration ≥1 year with daily insulin therapy ≥12 months and on insulin pump therapy for ≥6 months were randomized per night into PHHM mode or PLGS-only mode for 42 nights. The primary outcome was percentage of time in sensor-measured range 70 to 180 mg/dL in the overnight period.
The addition of automated insulin delivery with PHHM increased time in target range (70-180 mg/dL) from 66 ± 11% during PLGS nights to 76 ± 9% during PHHM nights (P<.001), without increasing hypoglycemia as measured by time below various thresholds. Average morning blood glucose improved from 176 ± 28 mg/dL following PLGS nights to 154 ± 19 mg/dL following PHHM nights (P<.001).
The PHHM system was effective in optimizing overnight glycemic control, significantly increasing time in range, lowering mean glucose, and decreasing glycemic variability compared to PLGS alone in children 6 to 14 years old.
Closed-loop insulin delivery systems are fast becoming the standard of care in the management of type 1 diabetes and have led to significant improvements in diabetes management. Nevertheless, there ...is still room for improvement for the closed-loop systems to optimize treatment and meet target glycemic control. Adjunct treatments have been introduced as an alternative method to insulin-only treatment methods to overcome diabetes treatment challenges and improve clinical and patient reported outcomes during closed-loop treatment. The adjunct treatment agents mostly consist of medications that are already approved for type 2 diabetes treatment and aim to complete the missing physiologic factors, such as the entero-endocrine system, that regulate glycemia in addition to insulin. This paper will review many of these adjunct therapies, including the basic mechanisms of action, potential benefits, side effects, and the evidence supporting their use during closed-loop treatment.
Using a continuous glucose monitor (CGM) improves glycemic control in patients with type 1 diabetes. The ambulatory glucose profile (AGP) has been recommended as a standard method for reporting CGM ...data. However, in recently developed automated insulin delivery (AID) systems, a standard format for reporting data has not yet been developed. Instead, reports are specific to each system being used. Currently, the only FDA approved AID system is a hybrid closed-loop insulin pump. In these systems, the patient is still required to announce a meal, respond to alerts, and keep the system in automated insulin delivery. The integrated pump and sensor information provides insights into how the system is performing, and how to make changes to tunable parameters, such as carbohydrate to insulin ratios. The reports also offer a window into human behavior related to performing diabetes tasks, responding to alarms, reasons for exiting HCL, and how glycemic goals are being met. This article reviews the pump and CGM data provided by several of the current closed-loop systems with a focus on systems that are currently approved in the United States (MiniMed™ 670G, Tandem Basal:IQ) and those used by patients using do-it-yourself systems. A step-wise approach to reviewing the nuances of these systems is provided. The comparison may reinforce the importance of the continued need for streamlining a standard report for providers to be able to interpret the CGM data of these systems.
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, ...general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
We investigated the impact of an extended bolus (EB) for a HFHP breakfast on postprandial glycemia in adolescents with T1D using the Control IQ (CIQ) system. In a randomized cross-over trial, ...following optimization, the participants with CIQ were asked to have identical standardized HFHP breakfasts (carbohydrate:51 g, fat:40 g, protein:54 g). Meal insulin bolus infusion began within 0.25 hrs of meal consumption (time 0). Eight adolescents (mean age 15.2 ± 1.7 yrs) were randomly assigned in blocks of two to the order in which they received the extended and standard insulin bolus (SB). Postprandial sensor glucose (SG) was measured for 5 hrs. The 5-hr incremental area under the curves (AUC) for SG was comparable for EB and SB (8913 and 10305 mg/dl/min, respectively). Peak SG value, time to peak SG, time in range SG (69.75% for SB vs. 71.7% for EB) and percent time >180 mg/dl were similar in both groups; however, the AUC for SG 2 hrs post meal onset and the rise in SG was significantly higher with EB (p= 0.02 and 0.03 respectively). Insulin use was not significantly different between arms. CL cannot fully compensate for a HFHP meal. However, SB appears to achieve better glycemic control than EB during the two hour postprandial period. Further studies with a larger sample size and larger and longer extended meal boluses may be needed to optimize pre-meal bolus dosing for HFHP meals.
Disclosure
L.Ekhlaspour: Consultant; Ypsomed AG, Tandem Diabetes Care, Inc., Other Relationship; NIH - National Institutes of Health, Research Support; MannKind Corporation, Tandem Diabetes Care, Inc., JDRF, Speaker's Bureau; Insulet Corporation. Y.J.Hosseinipour: None. B.A.Buckingham: Advisory Panel; Medtronic, Novo Nordisk, Consultant; Lilly, Research Support; Medtronic, Insulet Corporation, Tandem Diabetes Care, Inc. E.Cengiz: Advisory Panel; Eli Lilly and Company, Adocia, Novo Nordisk, Arecor.
Funding
National Institutes of Health (1K23DK121942)
The American Diabetes Association (ADA) "Standards of Care in Diabetes" includes the ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, ...general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, an interprofessional expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations and a full list of Professional Practice Committee members, please refer to Introduction and Methodology. Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.
This study evaluated a new insulin delivery system designed to reduce insulin delivery when trends in continuous glucose monitoring (CGM) glucose concentrations predict future hypoglycemia.
...Individuals with type 1 diabetes (
= 103, age 6-72 years, mean HbA
7.3% 56 mmol/mol) participated in a 6-week randomized crossover trial to evaluate the efficacy and safety of a Tandem Diabetes Care t:slim X2 pump with Basal-IQ integrated with a Dexcom G5 sensor and a predictive low-glucose suspend algorithm (PLGS) compared with sensor-augmented pump (SAP) therapy. The primary outcome was CGM-measured time <70 mg/dL.
Both study periods were completed by 99% of participants; median CGM usage exceeded 90% in both arms. Median time <70 mg/dL was reduced from 3.6% at baseline to 2.6% during the 3-week period in the PLGS arm compared with 3.2% in the SAP arm (difference PLGS - SAP = -0.8%, 95% CI -1.1 to -0.5,
< 0.001). The corresponding mean values were 4.4%, 3.1%, and 4.5%, respectively, represent-ing a 31% reduction in the time <70 mg/dL with PLGS. There was no increase in mean glucose concentration (159 vs. 159 mg/dL,
= 0.40) or percentage of time spent >180 mg/dL (32% vs. 33%,
= 0.12). One severe hypoglycemic event occurred in the SAP arm and none in the PLGS arm. Mean pump suspension time was 104 min/day.
The Tandem Diabetes Care Basal-IQ PLGS system significantly reduced hypoglycemia without rebound hyperglycemia, indicating that the system can benefit adults and youth with type 1 diabetes in improving glycemic control.
Abstract
The significant and growing global prevalence of diabetes continues to challenge people with diabetes (PwD), healthcare providers, and payers. While maintaining near-normal glucose levels ...has been shown to prevent or delay the progression of the long-term complications of diabetes, a significant proportion of PwD are not attaining their glycemic goals. During the past 6 years, we have seen tremendous advances in automated insulin delivery (AID) technologies. Numerous randomized controlled trials and real-world studies have shown that the use of AID systems is safe and effective in helping PwD achieve their long-term glycemic goals while reducing hypoglycemia risk. Thus, AID systems have recently become an integral part of diabetes management. However, recommendations for using AID systems in clinical settings have been lacking. Such guided recommendations are critical for AID success and acceptance. All clinicians working with PwD need to become familiar with the available systems in order to eliminate disparities in diabetes quality of care. This report provides much-needed guidance for clinicians who are interested in utilizing AIDs and presents a comprehensive listing of the evidence payers should consider when determining eligibility criteria for AID insurance coverage.
Graphical Abstract
Objective: Modeling the effect of meal composition on glucose excursion would help in designing decision support systems (DSS) for type 1 diabetes (T1D) management. In fact, macronutrients ...differently affect post-prandial gastric retention (GR), rate of appearance (R<inline-formula><tex-math notation="LaTeX">_\text{a}</tex-math></inline-formula>), and insulin sensitivity (S<inline-formula><tex-math notation="LaTeX">_\text{I}</tex-math></inline-formula>). Such variables can be estimated, in inpatient settings, from plasma glucose (G) and insulin (I) data using the Oral glucose Minimal Model (OMM) coupled with a physiological model of glucose transit through the gastrointestinal tract (reference OMM, R-OMM). Here, we present a model able to estimate those quantities in daily-life conditions, using minimally-invasive (MI) technologies, and validate it against the R-OMM. Methods: Forty-seven individuals with T1D (weight<inline-formula><tex-math notation="LaTeX">=78\pm</tex-math></inline-formula>13 kg, age<inline-formula><tex-math notation="LaTeX">=42\pm</tex-math></inline-formula>10 yr) underwent three 23-hour visits, during which G and I were frequently sampled while wearing continuous glucose monitoring (CGM) and insulin pump (IP). Using a Bayesian Maximum A Posteriori estimator, R-OMM was identified from plasma G and I measurements, and MI-OMM was identified from CGM and IP data. Results: The MI-OMM fitted the CGM data well and provided precise parameter estimates. GR and R<inline-formula><tex-math notation="LaTeX">_\text{a}</tex-math></inline-formula> model parameters were not significantly different using the MI-OMM and R-OMM (p<inline-formula><tex-math notation="LaTeX">></tex-math></inline-formula>0.05) and the correlation between the two S<inline-formula><tex-math notation="LaTeX">_\text{I}</tex-math></inline-formula> was satisfactory (<inline-formula><tex-math notation="LaTeX">\rho</tex-math></inline-formula><inline-formula><tex-math notation="LaTeX">=</tex-math></inline-formula>0.77). Conclusion: The MI-OMM is usable to estimate GR, R<inline-formula><tex-math notation="LaTeX">_\text{a}</tex-math></inline-formula>, and S<inline-formula><tex-math notation="LaTeX">_\text{I}</tex-math></inline-formula> from data collected in real-life conditions with minimally-invasive technologies. Significance: Applying MI-OMM to datasets where meal compositions are available will allow modeling the effect of each macronutrient on GR, R<inline-formula><tex-math notation="LaTeX">_\text{a}</tex-math></inline-formula>, and S<inline-formula><tex-math notation="LaTeX">_\text{I}</tex-math></inline-formula>. DSS could finally exploit this information to improve diabetes management.
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